In vivo murine lymph node imaging with multispectral photoacoustic technology and clinical contrast agents
Bibliographic record
Abstract
Here we describe the use of a commercially available photoacoustic (PA) imaging system (Vevo LAZR, VisualSonics, Toronto) to detect methylene blue (MB) and indocyanine green (ICG) in the left and right iliac lymph nodes after injection into the synovial space of the left and right knee joints. The PA imaging system generated light from a tunable laser (680 – 970 nm) which was delivered through fiber optic bundles integrated into a linear array transducer (LZ‐250, fc = 21 MHz), driven by a linear stepper motor for 3D imaging. Ultrasound image‐guided injections of 25ul injections of MB (10 mg/ml) and ICG (5 mg/ml) were performed by inserting a 30 gauge needle inserted into the synovial space of the left and right knee, respectively. Two‐dimensional regions of interest surrounding the left and right iliac lymph nodes and the abdominal aorta provided dye and blood absorption spectra. Comparison of pre‐injection and post‐injection multiplexed images clearly demonstrated ICG and MB in the left and right iliac lymph nodes, respectively. The absorbance spectra of the dyes demonstrated characteristic results with clear differences apparent for MB (peak at 680nm), ICG (peak at 800nm) and typical blood spectra. Photoacoustic imaging thus can be used to identify sentinel lymph nodes. With dyes targeted to biomarkers for cancer cells, the method could also be used to identify metastases there and elsewhere. Photoacoustic imaging should also be useful in the study of lymphatic function in other disease processes such as lymphedema, arthritis, and parasitic infections. All research was conducted and funded by VisualSonics, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".